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Users in a cooperative computing environment always have a tendency to free-ride. One can employ incentive mechanisms to prevent such behavior. Some of the cooperative computing scenarios have the same access link shared between upload and download. In such a situation, increasing upload capacity decreases the download capacity and vice versa. Optimal partitioning of link capacity between upload and download needs to be done by each user to maximize its gain (i.e., download) from the network. We model this link capacity partitioning problem as a feedback control system, where feedback (resources received) decides the number of resources to be uploaded by a user. The resulting algorithm called adaptive step size (ASZ) dynamically adjusts the partitioning of link capacity to an optimal value. To compare this approach with others, a metric “level of optimality (U)”is introduced. U achieved by the ASZ is closer to the optimal level than the reputation-based resource allocation policy (existing scheme), thus resulting in its better performance. The ASZ is also integrated with BitTorrent, and the simulation results show that it increases the resources received by the users. The ASZ can provide an efficient solution to the problem of optimal partitioning in real-life distributed networks due to its distributed implementation, robustness to changes in network dynamics, and compatibility with the existing partitioning schemes.

Abstract Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist. Deep Learning (DL) systems have shown promise in enhancing the objectivity and efficiency of Gleason grading. However, DL networks exhibit domain shift and reduced performance on Whole Slide Images (WSI) from a source other than training data. We propose a DL approach for segmenting and grading epithelial tissue using a novel training methodology that learns domain agnostic features. In this retrospective study, we analyzed WSI from three cohorts of prostate cancer patients. 3741 core needle biopsies (CNBs) received from two centers were used for training. The κquad (quadratic-weighted kappa) and AUC were measured for grade group comparison and core-level detection accuracy, respectively. Accuracy of 89.4% and κquad of 0.92 on the internal test set of 425 CNB WSI and accuracy of 85.3% and κquad of 0.96 on an external set of 1201 images, was observed. The system showed an accuracy of 83.1% and κquad of 0.93 on 1303 WSI from the third institution (blind evaluation). Our DL system, used as an assistive tool for CNB review, can potentially improve the consistency and accuracy of grading, resulting in better patient outcomes.

Aim: Data regarding the optimal management of metastatic anorectal melanoma (mARM) is scarce. The primary aim was to evaluate the potential benefits of systemic therapy in mARM. Materials and Methods: This is a retrospective analysis of all mARM who presented between July 2013 and June 2015 at the Department of GI Medical Oncology, Tata Memorial Hospital. Results: Of a total of 37 patients, twelve patients were planned for best supportive care (BSC) only while the remaining 25 patients received systemic therapy. The median overall survival (OS) for the whole cohort was 27 weeks. The OS was significantly better in patients who received first-line therapy as compared to those who were offered BSC (median OS: 14 vs. 33 weeks; P = 0.04). Patients with PS of 1 did significantly better than PS of 2 more (OS 70 vs. 17 weeks; P = 0.015). Conclusion: mARM should be offered chemotherapy, especially in good performance patients. Paclitaxel/Platinum or Capecitabine/Temozolomide regimens can be considered as the preferred regime in the resource-limited setting where immunotherapy may not be a feasible option.